Does Cp 1.33 guarantee Cpk 1.33?

Only if the process is perfectly centered. Any drift reduces Cpk while Cp stays at 1.33.

Capability Interpretation

Cp = 1.33: Minimum Potential Target

2 min read Last updated July 2026

Cp 1.33 means the process variation is 25% narrower than the specification width. Whether Cpk also hits 1.33 depends on centering.

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The engineering question this page answers

Cp is 1.33. What is the maximum Cpk this process can ever deliver, and what centering discipline is needed to reach it?

Decision logic

Cpk_max = Cp = 1.33 when perfectly centered ↓ If Cpk is far below 1.33 → centering opportunity ↓ If Cpk is near 1.33 already → variation is the next lever ↓ Do not target Cp 1.67 if 1.33 satisfies the CSR — improve the right thing

Typical PPM and sigma equivalent

Typical PPM: ≈ 66.1 PPM only if centered so Cpk also equals 1.33
Sigma equivalent: 3.99σ potential to each limit when centered

Engineering procedure

Compute k = |μ − target| / (tolerance/2); Cpk = Cp × (1 − k).
Identify the centering mechanism (tool offset, thermal, fixture datum).
Verify the improvement with a fresh study before closing the action.

Typical failure modes

Assuming Cp 1.33 guarantees Cpk 1.33.
Attacking variation while centering is still uncorrected.

Engineering insight

Cp 1.33 with Cpk 0.90 means roughly 32% centering loss — usually recoverable via offset, not tooling.
Between-shift centering drift often eats most of the Cp headroom before end-of-shift.

When NOT to use this metric

One-sided characteristics.

Relationship to other capability metrics

Cp 1.33 caps Cpk at 1.33 for the current variation.

Recommended actions

Use the free Process Capability Calculator to compute Cp, Cpk, Pp, Ppk, sigma level, and predicted PPM from your measurement data — no sign-up required.

Engineering notes

Report Cp and Cpk together; either alone is misleading.

Continue the investigation

For centering recovery, see Process Not Centered. For target selection, see Cpk = 1.33.

Verification checklist

Centering loss k quantified before action
Cp confirmed as the ceiling for the current variation

Assumptions and applicability

Process condition: statistical stability is required.
Distribution assumption: use a distribution model justified for the data.
Confirm process stability and measurement-system adequacy before interpreting a capability index.
Use a justified distribution model or non-normal method when the normal model is unsuitable.